Overview

Dataset statistics

Number of variables40
Number of observations18316
Missing cells136848
Missing cells (%)18.7%
Duplicate rows1568
Duplicate rows (%)8.6%
Total size in memory5.6 MiB
Average record size in memory320.0 B

Variable types

Numeric13
Text6
Categorical13
DateTime7
Unsupported1

Alerts

type_of_assessment has constant value ""Constant
v_type_of_assessment has constant value ""Constant
Dataset has 1568 (8.6%) duplicate rowsDuplicates
c_charge_degree is highly imbalanced (53.6%)Imbalance
is_violent_recid is highly imbalanced (62.3%)Imbalance
event is highly imbalanced (73.6%)Imbalance
id has 7315 (39.9%) missing valuesMissing
days_b_screening_arrest has 1297 (7.1%) missing valuesMissing
c_jail_in has 1297 (7.1%) missing valuesMissing
c_jail_out has 1297 (7.1%) missing valuesMissing
c_days_from_compas has 867 (4.7%) missing valuesMissing
c_charge_degree has 867 (4.7%) missing valuesMissing
c_charge_desc has 881 (4.8%) missing valuesMissing
r_charge_degree has 9899 (54.0%) missing valuesMissing
r_days_from_arrest has 11957 (65.3%) missing valuesMissing
r_offense_date has 9899 (54.0%) missing valuesMissing
r_charge_desc has 10039 (54.8%) missing valuesMissing
r_jail_in has 11957 (65.3%) missing valuesMissing
violent_recid has 18316 (100.0%) missing valuesMissing
vr_charge_degree has 16977 (92.7%) missing valuesMissing
vr_offense_date has 16977 (92.7%) missing valuesMissing
vr_charge_desc has 16977 (92.7%) missing valuesMissing
id is uniformly distributedUniform
violent_recid is an unsupported type, check if it needs cleaning or further analysisUnsupported
juv_fel_count has 17466 (95.4%) zerosZeros
juv_misd_count has 17206 (93.9%) zerosZeros
juv_other_count has 16804 (91.7%) zerosZeros
priors_count has 5175 (28.3%) zerosZeros
days_b_screening_arrest has 3107 (17.0%) zerosZeros
c_days_from_compas has 2087 (11.4%) zerosZeros
r_days_from_arrest has 4620 (25.2%) zerosZeros
priors_count.1 has 5175 (28.3%) zerosZeros

Reproduction

Analysis started2024-01-09 21:42:52.216679
Analysis finished2024-01-09 21:43:16.919738
Duration24.7 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

id
Real number (ℝ)

MISSING  UNIFORM 

Distinct11001
Distinct (%)100.0%
Missing7315
Missing (%)39.9%
Infinite0
Infinite (%)0.0%
Mean5501
Minimum1
Maximum11001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size143.2 KiB
2024-01-09T22:43:17.019032image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile551
Q12751
median5501
Q38251
95-th percentile10451
Maximum11001
Range11000
Interquartile range (IQR)5500

Descriptive statistics

Standard deviation3175.8595
Coefficient of variation (CV)0.57732403
Kurtosis-1.2
Mean5501
Median Absolute Deviation (MAD)2750
Skewness0
Sum60516501
Variance10086084
MonotonicityStrictly increasing
2024-01-09T22:43:17.148333image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7338 1
 
< 0.1%
7330 1
 
< 0.1%
7331 1
 
< 0.1%
7332 1
 
< 0.1%
7333 1
 
< 0.1%
7334 1
 
< 0.1%
7335 1
 
< 0.1%
7336 1
 
< 0.1%
7337 1
 
< 0.1%
7339 1
 
< 0.1%
Other values (10991) 10991
60.0%
(Missing) 7315
39.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
11001 1
< 0.1%
11000 1
< 0.1%
10999 1
< 0.1%
10998 1
< 0.1%
10997 1
< 0.1%
10996 1
< 0.1%
10995 1
< 0.1%
10994 1
< 0.1%
10993 1
< 0.1%
10992 1
< 0.1%

name
Text

Distinct10855
Distinct (%)59.3%
Missing0
Missing (%)0.0%
Memory size143.2 KiB
2024-01-09T22:43:17.336535image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length29
Median length27
Mean length13.69109
Min length6

Characters and Unicode

Total characters250766
Distinct characters32
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6731 ?
Unique (%)36.7%

Sample

1st rowmiguel hernandez
2nd rowmiguel hernandez
3rd rowmichael ryan
4th rowkevon dixon
5th rowed philo
ValueCountFrequency (%)
michael 422
 
1.2%
joseph 249
 
0.7%
christopher 245
 
0.7%
williams 240
 
0.7%
anthony 239
 
0.7%
james 233
 
0.6%
johnson 228
 
0.6%
john 218
 
0.6%
smith 209
 
0.6%
robert 205
 
0.6%
Other values (9066) 34179
93.2%
2024-01-09T22:43:17.668531image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 25231
 
10.1%
e 24781
 
9.9%
r 20275
 
8.1%
n 19404
 
7.7%
18351
 
7.3%
i 16192
 
6.5%
o 16001
 
6.4%
l 14402
 
5.7%
s 13459
 
5.4%
t 10260
 
4.1%
Other values (22) 72410
28.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 232239
92.6%
Space Separator 18351
 
7.3%
Dash Punctuation 169
 
0.1%
Other Punctuation 6
 
< 0.1%
Modifier Symbol 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 25231
 
10.9%
e 24781
 
10.7%
r 20275
 
8.7%
n 19404
 
8.4%
i 16192
 
7.0%
o 16001
 
6.9%
l 14402
 
6.2%
s 13459
 
5.8%
t 10260
 
4.4%
h 8476
 
3.6%
Other values (16) 63758
27.5%
Other Punctuation
ValueCountFrequency (%)
. 3
50.0%
' 2
33.3%
, 1
 
16.7%
Space Separator
ValueCountFrequency (%)
18351
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 169
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 232239
92.6%
Common 18527
 
7.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 25231
 
10.9%
e 24781
 
10.7%
r 20275
 
8.7%
n 19404
 
8.4%
i 16192
 
7.0%
o 16001
 
6.9%
l 14402
 
6.2%
s 13459
 
5.8%
t 10260
 
4.4%
h 8476
 
3.6%
Other values (16) 63758
27.5%
Common
ValueCountFrequency (%)
18351
99.1%
- 169
 
0.9%
. 3
 
< 0.1%
' 2
 
< 0.1%
` 1
 
< 0.1%
, 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 250766
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 25231
 
10.1%
e 24781
 
9.9%
r 20275
 
8.1%
n 19404
 
7.7%
18351
 
7.3%
i 16192
 
6.5%
o 16001
 
6.4%
l 14402
 
5.7%
s 13459
 
5.4%
t 10260
 
4.1%
Other values (22) 72410
28.9%

first
Text

Distinct3876
Distinct (%)21.2%
Missing0
Missing (%)0.0%
Memory size143.2 KiB
2024-01-09T22:43:17.864199image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length6.1700699
Min length2

Characters and Unicode

Total characters113011
Distinct characters31
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1911 ?
Unique (%)10.4%

Sample

1st rowmiguel
2nd rowmiguel
3rd rowmichael
4th rowkevon
5th rowed
ValueCountFrequency (%)
michael 421
 
2.3%
christopher 245
 
1.3%
anthony 233
 
1.3%
james 211
 
1.2%
john 209
 
1.1%
robert 202
 
1.1%
david 198
 
1.1%
daniel 167
 
0.9%
joseph 156
 
0.9%
jason 136
 
0.7%
Other values (3867) 16140
88.1%
2024-01-09T22:43:18.176100image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 13886
12.3%
e 12602
11.2%
n 10065
 
8.9%
r 9930
 
8.8%
i 8430
 
7.5%
o 6688
 
5.9%
l 6504
 
5.8%
s 5259
 
4.7%
t 5031
 
4.5%
h 4690
 
4.2%
Other values (21) 29926
26.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 112992
> 99.9%
Dash Punctuation 13
 
< 0.1%
Other Punctuation 3
 
< 0.1%
Space Separator 2
 
< 0.1%
Modifier Symbol 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 13886
12.3%
e 12602
11.2%
n 10065
 
8.9%
r 9930
 
8.8%
i 8430
 
7.5%
o 6688
 
5.9%
l 6504
 
5.8%
s 5259
 
4.7%
t 5031
 
4.5%
h 4690
 
4.2%
Other values (16) 29907
26.5%
Other Punctuation
ValueCountFrequency (%)
' 2
66.7%
. 1
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 13
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 112992
> 99.9%
Common 19
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 13886
12.3%
e 12602
11.2%
n 10065
 
8.9%
r 9930
 
8.8%
i 8430
 
7.5%
o 6688
 
5.9%
l 6504
 
5.8%
s 5259
 
4.7%
t 5031
 
4.5%
h 4690
 
4.2%
Other values (16) 29907
26.5%
Common
ValueCountFrequency (%)
- 13
68.4%
' 2
 
10.5%
2
 
10.5%
` 1
 
5.3%
. 1
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 113011
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 13886
12.3%
e 12602
11.2%
n 10065
 
8.9%
r 9930
 
8.8%
i 8430
 
7.5%
o 6688
 
5.9%
l 6504
 
5.8%
s 5259
 
4.7%
t 5031
 
4.5%
h 4690
 
4.2%
Other values (21) 29926
26.5%

last
Text

Distinct5635
Distinct (%)30.8%
Missing0
Missing (%)0.0%
Memory size143.2 KiB
2024-01-09T22:43:18.388901image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length20
Median length18
Mean length6.5210199
Min length1

Characters and Unicode

Total characters119439
Distinct characters34
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2910 ?
Unique (%)15.9%

Sample

1st rowhernandez
2nd rowhernandez
3rd rowryan
4th rowdixon
5th rowphilo
ValueCountFrequency (%)
williams 239
 
1.3%
johnson 224
 
1.2%
smith 209
 
1.1%
brown 205
 
1.1%
jones 169
 
0.9%
jackson 120
 
0.7%
davis 115
 
0.6%
wilson 94
 
0.5%
joseph 93
 
0.5%
robinson 86
 
0.5%
Other values (5622) 16795
91.5%
2024-01-09T22:43:18.733829image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 12176
 
10.2%
a 11345
 
9.5%
r 10342
 
8.7%
n 9339
 
7.8%
o 9313
 
7.8%
s 8200
 
6.9%
l 7898
 
6.6%
i 7762
 
6.5%
t 5226
 
4.4%
m 4360
 
3.7%
Other values (24) 33478
28.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 119235
99.8%
Dash Punctuation 156
 
0.1%
Space Separator 33
 
< 0.1%
Uppercase Letter 12
 
< 0.1%
Other Punctuation 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 12176
 
10.2%
a 11345
 
9.5%
r 10342
 
8.7%
n 9339
 
7.8%
o 9313
 
7.8%
s 8200
 
6.9%
l 7898
 
6.6%
i 7762
 
6.5%
t 5226
 
4.4%
m 4360
 
3.7%
Other values (16) 33274
27.9%
Uppercase Letter
ValueCountFrequency (%)
T 3
25.0%
R 3
25.0%
U 3
25.0%
E 3
25.0%
Other Punctuation
ValueCountFrequency (%)
. 2
66.7%
, 1
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 156
100.0%
Space Separator
ValueCountFrequency (%)
33
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 119247
99.8%
Common 192
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 12176
 
10.2%
a 11345
 
9.5%
r 10342
 
8.7%
n 9339
 
7.8%
o 9313
 
7.8%
s 8200
 
6.9%
l 7898
 
6.6%
i 7762
 
6.5%
t 5226
 
4.4%
m 4360
 
3.7%
Other values (20) 33286
27.9%
Common
ValueCountFrequency (%)
- 156
81.2%
33
 
17.2%
. 2
 
1.0%
, 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 119439
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 12176
 
10.2%
a 11345
 
9.5%
r 10342
 
8.7%
n 9339
 
7.8%
o 9313
 
7.8%
s 8200
 
6.9%
l 7898
 
6.6%
i 7762
 
6.5%
t 5226
 
4.4%
m 4360
 
3.7%
Other values (24) 33478
28.0%

sex
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size143.2 KiB
Male
14933 
Female
3383 

Length

Max length6
Median length4
Mean length4.3694038
Min length4

Characters and Unicode

Total characters80030
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowMale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Male 14933
81.5%
Female 3383
 
18.5%

Length

2024-01-09T22:43:18.884327image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-09T22:43:18.991043image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
male 14933
81.5%
female 3383
 
18.5%

Most occurring characters

ValueCountFrequency (%)
e 21699
27.1%
a 18316
22.9%
l 18316
22.9%
M 14933
18.7%
F 3383
 
4.2%
m 3383
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 61714
77.1%
Uppercase Letter 18316
 
22.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 21699
35.2%
a 18316
29.7%
l 18316
29.7%
m 3383
 
5.5%
Uppercase Letter
ValueCountFrequency (%)
M 14933
81.5%
F 3383
 
18.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 80030
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 21699
27.1%
a 18316
22.9%
l 18316
22.9%
M 14933
18.7%
F 3383
 
4.2%
m 3383
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80030
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 21699
27.1%
a 18316
22.9%
l 18316
22.9%
M 14933
18.7%
F 3383
 
4.2%
m 3383
 
4.2%

dob
Date

Distinct7485
Distinct (%)40.9%
Missing0
Missing (%)0.0%
Memory size143.2 KiB
Minimum1919-10-14 00:00:00
Maximum1998-03-29 00:00:00
2024-01-09T22:43:19.105256image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:19.250582image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

age
Real number (ℝ)

Distinct65
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.019273
Minimum18
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size143.2 KiB
2024-01-09T22:43:19.381270image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile21
Q125
median31
Q341
95-th percentile57
Maximum96
Range78
Interquartile range (IQR)16

Descriptive statistics

Standard deviation11.667811
Coefficient of variation (CV)0.34297651
Kurtosis0.22721798
Mean34.019273
Median Absolute Deviation (MAD)7
Skewness0.96321103
Sum623097
Variance136.13782
MonotonicityNot monotonic
2024-01-09T22:43:19.519512image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22 915
 
5.0%
26 911
 
5.0%
24 911
 
5.0%
21 900
 
4.9%
25 890
 
4.9%
27 853
 
4.7%
23 837
 
4.6%
29 728
 
4.0%
30 723
 
3.9%
28 703
 
3.8%
Other values (55) 9945
54.3%
ValueCountFrequency (%)
18 14
 
0.1%
19 118
 
0.6%
20 588
3.2%
21 900
4.9%
22 915
5.0%
23 837
4.6%
24 911
5.0%
25 890
4.9%
26 911
5.0%
27 853
4.7%
ValueCountFrequency (%)
96 2
 
< 0.1%
83 2
 
< 0.1%
80 1
 
< 0.1%
79 1
 
< 0.1%
78 2
 
< 0.1%
77 8
< 0.1%
76 5
< 0.1%
75 9
< 0.1%
74 5
< 0.1%
73 4
< 0.1%

age_cat
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size143.2 KiB
25 - 45
10372 
Less than 25
4283 
Greater than 45
3661 

Length

Max length15
Median length7
Mean length9.7682354
Min length7

Characters and Unicode

Total characters178915
Distinct characters14
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGreater than 45
2nd rowGreater than 45
3rd row25 - 45
4th row25 - 45
5th rowLess than 25

Common Values

ValueCountFrequency (%)
25 - 45 10372
56.6%
Less than 25 4283
23.4%
Greater than 45 3661
 
20.0%

Length

2024-01-09T22:43:19.659256image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-09T22:43:19.770584image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
25 14655
26.7%
45 14033
25.5%
10372
18.9%
than 7944
14.5%
less 4283
 
7.8%
greater 3661
 
6.7%

Most occurring characters

ValueCountFrequency (%)
36632
20.5%
5 28688
16.0%
2 14655
8.2%
4 14033
 
7.8%
e 11605
 
6.5%
t 11605
 
6.5%
a 11605
 
6.5%
- 10372
 
5.8%
s 8566
 
4.8%
h 7944
 
4.4%
Other values (4) 23210
13.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 66591
37.2%
Decimal Number 57376
32.1%
Space Separator 36632
20.5%
Dash Punctuation 10372
 
5.8%
Uppercase Letter 7944
 
4.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 11605
17.4%
t 11605
17.4%
a 11605
17.4%
s 8566
12.9%
h 7944
11.9%
n 7944
11.9%
r 7322
11.0%
Decimal Number
ValueCountFrequency (%)
5 28688
50.0%
2 14655
25.5%
4 14033
24.5%
Uppercase Letter
ValueCountFrequency (%)
L 4283
53.9%
G 3661
46.1%
Space Separator
ValueCountFrequency (%)
36632
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10372
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 104380
58.3%
Latin 74535
41.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 11605
15.6%
t 11605
15.6%
a 11605
15.6%
s 8566
11.5%
h 7944
10.7%
n 7944
10.7%
r 7322
9.8%
L 4283
 
5.7%
G 3661
 
4.9%
Common
ValueCountFrequency (%)
36632
35.1%
5 28688
27.5%
2 14655
14.0%
4 14033
 
13.4%
- 10372
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 178915
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
36632
20.5%
5 28688
16.0%
2 14655
8.2%
4 14033
 
7.8%
e 11605
 
6.5%
t 11605
 
6.5%
a 11605
 
6.5%
- 10372
 
5.8%
s 8566
 
4.8%
h 7944
 
4.4%
Other values (4) 23210
13.0%

race
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size143.2 KiB
African-American
9791 
Caucasian
6086 
Hispanic
1451 
Other
 
860
Asian
 
71

Length

Max length16
Median length16
Mean length12.478052
Min length5

Characters and Unicode

Total characters228548
Distinct characters21
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOther
2nd rowOther
3rd rowCaucasian
4th rowAfrican-American
5th rowAfrican-American

Common Values

ValueCountFrequency (%)
African-American 9791
53.5%
Caucasian 6086
33.2%
Hispanic 1451
 
7.9%
Other 860
 
4.7%
Asian 71
 
0.4%
Native American 57
 
0.3%

Length

2024-01-09T22:43:19.882480image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-09T22:43:19.991406image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
african-american 9791
53.3%
caucasian 6086
33.1%
hispanic 1451
 
7.9%
other 860
 
4.7%
asian 71
 
0.4%
native 57
 
0.3%
american 57
 
0.3%

Most occurring characters

ValueCountFrequency (%)
a 39476
17.3%
i 28755
12.6%
n 27247
11.9%
c 27176
11.9%
r 20499
9.0%
A 19710
8.6%
e 10765
 
4.7%
m 9848
 
4.3%
- 9791
 
4.3%
f 9791
 
4.3%
Other values (11) 25490
11.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 190536
83.4%
Uppercase Letter 28164
 
12.3%
Dash Punctuation 9791
 
4.3%
Space Separator 57
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 39476
20.7%
i 28755
15.1%
n 27247
14.3%
c 27176
14.3%
r 20499
10.8%
e 10765
 
5.6%
m 9848
 
5.2%
f 9791
 
5.1%
s 7608
 
4.0%
u 6086
 
3.2%
Other values (4) 3285
 
1.7%
Uppercase Letter
ValueCountFrequency (%)
A 19710
70.0%
C 6086
 
21.6%
H 1451
 
5.2%
O 860
 
3.1%
N 57
 
0.2%
Dash Punctuation
ValueCountFrequency (%)
- 9791
100.0%
Space Separator
ValueCountFrequency (%)
57
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 218700
95.7%
Common 9848
 
4.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 39476
18.1%
i 28755
13.1%
n 27247
12.5%
c 27176
12.4%
r 20499
9.4%
A 19710
9.0%
e 10765
 
4.9%
m 9848
 
4.5%
f 9791
 
4.5%
s 7608
 
3.5%
Other values (9) 17825
8.2%
Common
ValueCountFrequency (%)
- 9791
99.4%
57
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 228548
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 39476
17.3%
i 28755
12.6%
n 27247
11.9%
c 27176
11.9%
r 20499
9.0%
A 19710
8.6%
e 10765
 
4.7%
m 9848
 
4.3%
- 9791
 
4.3%
f 9791
 
4.3%
Other values (11) 25490
11.2%

juv_fel_count
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.07687268
Minimum0
Maximum20
Zeros17466
Zeros (%)95.4%
Negative0
Negative (%)0.0%
Memory size143.2 KiB
2024-01-09T22:43:20.102710image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum20
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.46427157
Coefficient of variation (CV)6.0394873
Kurtosis322.92162
Mean0.07687268
Median Absolute Deviation (MAD)0
Skewness13.274474
Sum1408
Variance0.21554809
MonotonicityNot monotonic
2024-01-09T22:43:20.216814image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 17466
95.4%
1 554
 
3.0%
2 182
 
1.0%
3 59
 
0.3%
4 28
 
0.2%
5 12
 
0.1%
8 4
 
< 0.1%
10 4
 
< 0.1%
6 3
 
< 0.1%
9 2
 
< 0.1%
Other values (2) 2
 
< 0.1%
ValueCountFrequency (%)
0 17466
95.4%
1 554
 
3.0%
2 182
 
1.0%
3 59
 
0.3%
4 28
 
0.2%
5 12
 
0.1%
6 3
 
< 0.1%
8 4
 
< 0.1%
9 2
 
< 0.1%
10 4
 
< 0.1%
ValueCountFrequency (%)
20 1
 
< 0.1%
13 1
 
< 0.1%
10 4
 
< 0.1%
9 2
 
< 0.1%
8 4
 
< 0.1%
6 3
 
< 0.1%
5 12
 
0.1%
4 28
 
0.2%
3 59
 
0.3%
2 182
1.0%

decile_score
Real number (ℝ)

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9970518
Minimum-1
Maximum10
Zeros0
Zeros (%)0.0%
Negative23
Negative (%)0.1%
Memory size143.2 KiB
2024-01-09T22:43:20.325544image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q12
median5
Q38
95-th percentile10
Maximum10
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation2.937569
Coefficient of variation (CV)0.58786043
Kurtosis-1.2452923
Mean4.9970518
Median Absolute Deviation (MAD)3
Skewness0.15158307
Sum91526
Variance8.6293115
MonotonicityNot monotonic
2024-01-09T22:43:20.427028image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 2923
16.0%
2 2031
11.1%
4 1830
10.0%
3 1813
9.9%
7 1720
9.4%
6 1713
9.4%
9 1670
9.1%
5 1649
9.0%
8 1614
8.8%
10 1330
7.3%
ValueCountFrequency (%)
-1 23
 
0.1%
1 2923
16.0%
2 2031
11.1%
3 1813
9.9%
4 1830
10.0%
5 1649
9.0%
6 1713
9.4%
7 1720
9.4%
8 1614
8.8%
9 1670
9.1%
ValueCountFrequency (%)
10 1330
7.3%
9 1670
9.1%
8 1614
8.8%
7 1720
9.4%
6 1713
9.4%
5 1649
9.0%
4 1830
10.0%
3 1813
9.9%
2 2031
11.1%
1 2923
16.0%

juv_misd_count
Real number (ℝ)

ZEROS 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.098438524
Minimum0
Maximum13
Zeros17206
Zeros (%)93.9%
Negative0
Negative (%)0.0%
Memory size143.2 KiB
2024-01-09T22:43:20.524791image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum13
Range13
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.51763946
Coefficient of variation (CV)5.2585049
Kurtosis163.91777
Mean0.098438524
Median Absolute Deviation (MAD)0
Skewness10.327475
Sum1803
Variance0.26795061
MonotonicityNot monotonic
2024-01-09T22:43:20.637183image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 17206
93.9%
1 756
 
4.1%
2 218
 
1.2%
3 67
 
0.4%
4 30
 
0.2%
8 11
 
0.1%
6 10
 
0.1%
5 8
 
< 0.1%
12 4
 
< 0.1%
7 4
 
< 0.1%
ValueCountFrequency (%)
0 17206
93.9%
1 756
 
4.1%
2 218
 
1.2%
3 67
 
0.4%
4 30
 
0.2%
5 8
 
< 0.1%
6 10
 
0.1%
7 4
 
< 0.1%
8 11
 
0.1%
12 4
 
< 0.1%
ValueCountFrequency (%)
13 2
 
< 0.1%
12 4
 
< 0.1%
8 11
 
0.1%
7 4
 
< 0.1%
6 10
 
0.1%
5 8
 
< 0.1%
4 30
 
0.2%
3 67
 
0.4%
2 218
 
1.2%
1 756
4.1%

juv_other_count
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12426294
Minimum0
Maximum17
Zeros16804
Zeros (%)91.7%
Negative0
Negative (%)0.0%
Memory size143.2 KiB
2024-01-09T22:43:20.738260image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum17
Range17
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.52453729
Coefficient of variation (CV)4.2211885
Kurtosis135.26682
Mean0.12426294
Median Absolute Deviation (MAD)0
Skewness8.4596092
Sum2276
Variance0.27513937
MonotonicityNot monotonic
2024-01-09T22:43:20.842907image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 16804
91.7%
1 1063
 
5.8%
2 288
 
1.6%
3 93
 
0.5%
4 42
 
0.2%
5 10
 
0.1%
7 5
 
< 0.1%
9 3
 
< 0.1%
11 3
 
< 0.1%
6 3
 
< 0.1%
Other values (2) 2
 
< 0.1%
ValueCountFrequency (%)
0 16804
91.7%
1 1063
 
5.8%
2 288
 
1.6%
3 93
 
0.5%
4 42
 
0.2%
5 10
 
0.1%
6 3
 
< 0.1%
7 5
 
< 0.1%
9 3
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
17 1
 
< 0.1%
11 3
 
< 0.1%
10 1
 
< 0.1%
9 3
 
< 0.1%
7 5
 
< 0.1%
6 3
 
< 0.1%
5 10
 
0.1%
4 42
 
0.2%
3 93
 
0.5%
2 288
1.6%

priors_count
Real number (ℝ)

ZEROS 

Distinct39
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9131907
Minimum0
Maximum43
Zeros5175
Zeros (%)28.3%
Negative0
Negative (%)0.0%
Memory size143.2 KiB
2024-01-09T22:43:20.966612image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q35
95-th percentile15
Maximum43
Range43
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.2998641
Coefficient of variation (CV)1.3543588
Kurtosis5.9470361
Mean3.9131907
Median Absolute Deviation (MAD)2
Skewness2.2097383
Sum71674
Variance28.08856
MonotonicityNot monotonic
2024-01-09T22:43:21.082602image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0 5175
28.3%
1 3212
17.5%
2 2079
11.4%
3 1446
 
7.9%
4 1014
 
5.5%
5 897
 
4.9%
6 654
 
3.6%
7 607
 
3.3%
8 509
 
2.8%
9 456
 
2.5%
Other values (29) 2267
12.4%
ValueCountFrequency (%)
0 5175
28.3%
1 3212
17.5%
2 2079
11.4%
3 1446
 
7.9%
4 1014
 
5.5%
5 897
 
4.9%
6 654
 
3.6%
7 607
 
3.3%
8 509
 
2.8%
9 456
 
2.5%
ValueCountFrequency (%)
43 2
 
< 0.1%
39 1
 
< 0.1%
38 6
 
< 0.1%
37 1
 
< 0.1%
36 5
 
< 0.1%
35 3
 
< 0.1%
33 11
0.1%
31 14
0.1%
30 10
0.1%
29 20
0.1%

days_b_screening_arrest
Real number (ℝ)

MISSING  ZEROS 

Distinct534
Distinct (%)3.1%
Missing1297
Missing (%)7.1%
Infinite0
Infinite (%)0.0%
Mean4.3024855
Minimum-597
Maximum1057
Zeros3107
Zeros (%)17.0%
Negative12698
Negative (%)69.3%
Memory size143.2 KiB
2024-01-09T22:43:21.200408image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-597
5-th percentile-47
Q1-1
median-1
Q30
95-th percentile43
Maximum1057
Range1654
Interquartile range (IQR)1

Descriptive statistics

Standard deviation81.159881
Coefficient of variation (CV)18.863488
Kurtosis51.399708
Mean4.3024855
Median Absolute Deviation (MAD)0
Skewness5.2102096
Sum73224
Variance6586.9264
MonotonicityNot monotonic
2024-01-09T22:43:21.601208image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 9676
52.8%
0 3107
 
17.0%
-2 365
 
2.0%
-3 194
 
1.1%
-4 145
 
0.8%
-5 67
 
0.4%
-22 66
 
0.4%
-6 65
 
0.4%
-23 63
 
0.3%
-8 60
 
0.3%
Other values (524) 3211
 
17.5%
(Missing) 1297
 
7.1%
ValueCountFrequency (%)
-597 1
< 0.1%
-578 1
< 0.1%
-546 1
< 0.1%
-544 1
< 0.1%
-542 1
< 0.1%
-529 1
< 0.1%
-525 1
< 0.1%
-520 1
< 0.1%
-519 1
< 0.1%
-499 2
< 0.1%
ValueCountFrequency (%)
1057 2
< 0.1%
1040 2
< 0.1%
1027 2
< 0.1%
1026 2
< 0.1%
1025 2
< 0.1%
1001 2
< 0.1%
982 2
< 0.1%
841 2
< 0.1%
812 2
< 0.1%
805 4
< 0.1%

c_jail_in
Date

MISSING 

Distinct9806
Distinct (%)57.6%
Missing1297
Missing (%)7.1%
Memory size143.2 KiB
Minimum2013-01-01 01:31:00
Maximum2016-11-03 10:26:00
2024-01-09T22:43:21.733476image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:21.874344image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

c_jail_out
Date

MISSING 

Distinct8714
Distinct (%)51.2%
Missing1297
Missing (%)7.1%
Memory size143.2 KiB
Minimum2013-01-02 01:46:00
Maximum2020-01-01 00:00:00
2024-01-09T22:43:22.012720image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:22.145868image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

c_days_from_compas
Real number (ℝ)

MISSING  ZEROS 

Distinct657
Distinct (%)3.8%
Missing867
Missing (%)4.7%
Infinite0
Infinite (%)0.0%
Mean57.694596
Minimum0
Maximum9485
Zeros2087
Zeros (%)11.4%
Negative0
Negative (%)0.0%
Memory size143.2 KiB
2024-01-09T22:43:22.278648image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile230
Maximum9485
Range9485
Interquartile range (IQR)1

Descriptive statistics

Standard deviation317.99469
Coefficient of variation (CV)5.5116894
Kurtosis210.83583
Mean57.694596
Median Absolute Deviation (MAD)0
Skewness12.371435
Sum1006713
Variance101120.63
MonotonicityNot monotonic
2024-01-09T22:43:22.413463image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 10641
58.1%
0 2087
 
11.4%
2 521
 
2.8%
3 205
 
1.1%
4 177
 
1.0%
5 109
 
0.6%
23 77
 
0.4%
6 74
 
0.4%
7 68
 
0.4%
22 65
 
0.4%
Other values (647) 3425
 
18.7%
(Missing) 867
 
4.7%
ValueCountFrequency (%)
0 2087
 
11.4%
1 10641
58.1%
2 521
 
2.8%
3 205
 
1.1%
4 177
 
1.0%
5 109
 
0.6%
6 74
 
0.4%
7 68
 
0.4%
8 54
 
0.3%
9 44
 
0.2%
ValueCountFrequency (%)
9485 1
 
< 0.1%
8023 1
 
< 0.1%
7604 1
 
< 0.1%
6594 1
 
< 0.1%
6323 3
< 0.1%
5935 1
 
< 0.1%
5806 1
 
< 0.1%
5519 1
 
< 0.1%
5450 2
< 0.1%
5185 1
 
< 0.1%

c_charge_degree
Categorical

IMBALANCE  MISSING 

Distinct14
Distinct (%)0.1%
Missing867
Missing (%)4.7%
Memory size143.2 KiB
(F3)
10294 
(M1)
3681 
(F2)
1503 
(M2)
1283 
(F1)
 
298
Other values (9)
 
390

Length

Max length5
Median length4
Mean length4.0100292
Min length3

Characters and Unicode

Total characters69971
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row(F3)
2nd row(F3)
3rd row(F3)
4th row(F3)
5th row(F3)

Common Values

ValueCountFrequency (%)
(F3) 10294
56.2%
(M1) 3681
 
20.1%
(F2) 1503
 
8.2%
(M2) 1283
 
7.0%
(F1) 298
 
1.6%
(F7) 184
 
1.0%
(MO3) 154
 
0.8%
(NI0) 15
 
0.1%
(F6) 15
 
0.1%
(F5) 13
 
0.1%
Other values (4) 9
 
< 0.1%
(Missing) 867
 
4.7%

Length

2024-01-09T22:43:22.545449image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
f3 10294
59.0%
m1 3681
 
21.1%
f2 1503
 
8.6%
m2 1283
 
7.4%
f1 298
 
1.7%
f7 184
 
1.1%
mo3 154
 
0.9%
ni0 15
 
0.1%
f6 15
 
0.1%
f5 13
 
0.1%
Other values (4) 9
 
0.1%

Most occurring characters

ValueCountFrequency (%)
( 17449
24.9%
) 17449
24.9%
F 12307
17.6%
3 10454
14.9%
M 5118
 
7.3%
1 3979
 
5.7%
2 2786
 
4.0%
7 184
 
0.3%
O 160
 
0.2%
N 15
 
< 0.1%
Other values (7) 70
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 17627
25.2%
Open Punctuation 17449
24.9%
Close Punctuation 17449
24.9%
Decimal Number 17446
24.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 12307
69.8%
M 5118
29.0%
O 160
 
0.9%
N 15
 
0.1%
I 15
 
0.1%
C 8
 
< 0.1%
X 2
 
< 0.1%
T 2
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
3 10454
59.9%
1 3979
 
22.8%
2 2786
 
16.0%
7 184
 
1.1%
0 15
 
0.1%
6 15
 
0.1%
5 13
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 17449
100.0%
Close Punctuation
ValueCountFrequency (%)
) 17449
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 52344
74.8%
Latin 17627
 
25.2%

Most frequent character per script

Common
ValueCountFrequency (%)
( 17449
33.3%
) 17449
33.3%
3 10454
20.0%
1 3979
 
7.6%
2 2786
 
5.3%
7 184
 
0.4%
0 15
 
< 0.1%
6 15
 
< 0.1%
5 13
 
< 0.1%
Latin
ValueCountFrequency (%)
F 12307
69.8%
M 5118
29.0%
O 160
 
0.9%
N 15
 
0.1%
I 15
 
0.1%
C 8
 
< 0.1%
X 2
 
< 0.1%
T 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69971
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 17449
24.9%
) 17449
24.9%
F 12307
17.6%
3 10454
14.9%
M 5118
 
7.3%
1 3979
 
5.7%
2 2786
 
4.0%
7 184
 
0.3%
O 160
 
0.2%
N 15
 
< 0.1%
Other values (7) 70
 
0.1%

c_charge_desc
Text

MISSING 

Distinct513
Distinct (%)2.9%
Missing881
Missing (%)4.8%
Memory size143.2 KiB
2024-01-09T22:43:22.753430image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length52
Median length40
Mean length22.744135
Min length5

Characters and Unicode

Total characters396544
Distinct characters72
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique134 ?
Unique (%)0.8%

Sample

1st rowAggravated Assault w/Firearm
2nd rowAggravated Assault w/Firearm
3rd rowFelony Battery w/Prior Convict
4th rowPossession of Cocaine
5th rowPossession of Cocaine
ValueCountFrequency (%)
no 3258
 
5.4%
battery 3122
 
5.1%
arrest 3090
 
5.1%
charge 3089
 
5.1%
case 3085
 
5.1%
of 2451
 
4.0%
possession 2250
 
3.7%
theft 1919
 
3.2%
cocaine 1567
 
2.6%
grand 1532
 
2.5%
Other values (765) 35386
58.2%
2024-01-09T22:43:23.107860image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 44924
 
11.3%
43362
 
10.9%
r 29189
 
7.4%
a 26375
 
6.7%
s 25154
 
6.3%
n 24777
 
6.2%
o 22353
 
5.6%
t 21819
 
5.5%
i 18616
 
4.7%
c 14813
 
3.7%
Other values (62) 125162
31.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 299149
75.4%
Uppercase Letter 44545
 
11.2%
Space Separator 43362
 
10.9%
Decimal Number 3926
 
1.0%
Other Punctuation 3809
 
1.0%
Close Punctuation 598
 
0.2%
Open Punctuation 598
 
0.2%
Currency Symbol 233
 
0.1%
Math Symbol 176
 
< 0.1%
Dash Punctuation 148
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 44924
15.0%
r 29189
9.8%
a 26375
8.8%
s 25154
8.4%
n 24777
8.3%
o 22353
 
7.5%
t 21819
 
7.3%
i 18616
 
6.2%
c 14813
 
5.0%
g 10429
 
3.5%
Other values (16) 60700
20.3%
Uppercase Letter
ValueCountFrequency (%)
D 5270
11.8%
P 5256
11.8%
B 4371
 
9.8%
C 4017
 
9.0%
T 2794
 
6.3%
W 2357
 
5.3%
A 2153
 
4.8%
I 2115
 
4.7%
O 2098
 
4.7%
L 2006
 
4.5%
Other values (14) 12108
27.2%
Decimal Number
ValueCountFrequency (%)
3 1520
38.7%
0 1028
26.2%
1 472
 
12.0%
2 375
 
9.6%
4 257
 
6.5%
5 189
 
4.8%
6 70
 
1.8%
9 7
 
0.2%
8 6
 
0.2%
7 2
 
0.1%
Other Punctuation
ValueCountFrequency (%)
/ 3442
90.4%
, 236
 
6.2%
. 127
 
3.3%
# 4
 
0.1%
Math Symbol
ValueCountFrequency (%)
< 74
42.0%
+ 64
36.4%
> 38
21.6%
Space Separator
ValueCountFrequency (%)
43362
100.0%
Close Punctuation
ValueCountFrequency (%)
) 598
100.0%
Open Punctuation
ValueCountFrequency (%)
( 598
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 233
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 148
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 343694
86.7%
Common 52850
 
13.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 44924
13.1%
r 29189
 
8.5%
a 26375
 
7.7%
s 25154
 
7.3%
n 24777
 
7.2%
o 22353
 
6.5%
t 21819
 
6.3%
i 18616
 
5.4%
c 14813
 
4.3%
g 10429
 
3.0%
Other values (40) 105245
30.6%
Common
ValueCountFrequency (%)
43362
82.0%
/ 3442
 
6.5%
3 1520
 
2.9%
0 1028
 
1.9%
) 598
 
1.1%
( 598
 
1.1%
1 472
 
0.9%
2 375
 
0.7%
4 257
 
0.5%
, 236
 
0.4%
Other values (12) 962
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 396544
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 44924
 
11.3%
43362
 
10.9%
r 29189
 
7.4%
a 26375
 
6.7%
s 25154
 
6.3%
n 24777
 
6.2%
o 22353
 
5.6%
t 21819
 
5.5%
i 18616
 
4.7%
c 14813
 
3.7%
Other values (62) 125162
31.6%

is_recid
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size143.2 KiB
0
9079 
1
8417 
-1
 
820

Length

Max length2
Median length1
Mean length1.0447696
Min length1

Characters and Unicode

Total characters19136
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row-1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 9079
49.6%
1 8417
46.0%
-1 820
 
4.5%

Length

2024-01-09T22:43:23.252901image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-09T22:43:23.352777image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9237
50.4%
0 9079
49.6%

Most occurring characters

ValueCountFrequency (%)
1 9237
48.3%
0 9079
47.4%
- 820
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 18316
95.7%
Dash Punctuation 820
 
4.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 9237
50.4%
0 9079
49.6%
Dash Punctuation
ValueCountFrequency (%)
- 820
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 19136
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 9237
48.3%
0 9079
47.4%
- 820
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19136
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 9237
48.3%
0 9079
47.4%
- 820
 
4.3%

r_charge_degree
Categorical

MISSING 

Distinct10
Distinct (%)0.1%
Missing9899
Missing (%)54.0%
Memory size143.2 KiB
(M1)
2922 
(M2)
2590 
(F3)
2315 
(F2)
332 
(MO3)
 
133
Other values (5)
 
125

Length

Max length5
Median length4
Mean length4.0169894
Min length4

Characters and Unicode

Total characters33811
Distinct characters12
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row(F3)
2nd row(M1)
3rd row(M1)
4th row(M1)
5th row(M1)

Common Values

ValueCountFrequency (%)
(M1) 2922
 
16.0%
(M2) 2590
 
14.1%
(F3) 2315
 
12.6%
(F2) 332
 
1.8%
(MO3) 133
 
0.7%
(F1) 103
 
0.6%
(CO3) 10
 
0.1%
(F7) 8
 
< 0.1%
(F6) 3
 
< 0.1%
(F5) 1
 
< 0.1%
(Missing) 9899
54.0%

Length

2024-01-09T22:43:23.453814image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-09T22:43:23.570627image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
m1 2922
34.7%
m2 2590
30.8%
f3 2315
27.5%
f2 332
 
3.9%
mo3 133
 
1.6%
f1 103
 
1.2%
co3 10
 
0.1%
f7 8
 
0.1%
f6 3
 
< 0.1%
f5 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
( 8417
24.9%
) 8417
24.9%
M 5645
16.7%
1 3025
 
8.9%
2 2922
 
8.6%
F 2762
 
8.2%
3 2458
 
7.3%
O 143
 
0.4%
C 10
 
< 0.1%
7 8
 
< 0.1%
Other values (2) 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 8560
25.3%
Open Punctuation 8417
24.9%
Close Punctuation 8417
24.9%
Decimal Number 8417
24.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3025
35.9%
2 2922
34.7%
3 2458
29.2%
7 8
 
0.1%
6 3
 
< 0.1%
5 1
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
M 5645
65.9%
F 2762
32.3%
O 143
 
1.7%
C 10
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 8417
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8417
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 25251
74.7%
Latin 8560
 
25.3%

Most frequent character per script

Common
ValueCountFrequency (%)
( 8417
33.3%
) 8417
33.3%
1 3025
 
12.0%
2 2922
 
11.6%
3 2458
 
9.7%
7 8
 
< 0.1%
6 3
 
< 0.1%
5 1
 
< 0.1%
Latin
ValueCountFrequency (%)
M 5645
65.9%
F 2762
32.3%
O 143
 
1.7%
C 10
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33811
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 8417
24.9%
) 8417
24.9%
M 5645
16.7%
1 3025
 
8.9%
2 2922
 
8.6%
F 2762
 
8.2%
3 2458
 
7.3%
O 143
 
0.4%
C 10
 
< 0.1%
7 8
 
< 0.1%
Other values (2) 4
 
< 0.1%

r_days_from_arrest
Real number (ℝ)

MISSING  ZEROS 

Distinct201
Distinct (%)3.2%
Missing11957
Missing (%)65.3%
Infinite0
Infinite (%)0.0%
Mean19.961629
Minimum-1
Maximum993
Zeros4620
Zeros (%)25.2%
Negative12
Negative (%)0.1%
Memory size143.2 KiB
2024-01-09T22:43:23.715925image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q10
median0
Q31
95-th percentile136
Maximum993
Range994
Interquartile range (IQR)1

Descriptive statistics

Standard deviation74.139695
Coefficient of variation (CV)3.7141104
Kurtosis51.660047
Mean19.961629
Median Absolute Deviation (MAD)0
Skewness6.2640975
Sum126936
Variance5496.6943
MonotonicityNot monotonic
2024-01-09T22:43:23.848138image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4620
 
25.2%
1 769
 
4.2%
78 20
 
0.1%
51 20
 
0.1%
59 17
 
0.1%
36 17
 
0.1%
21 16
 
0.1%
77 16
 
0.1%
39 14
 
0.1%
18 14
 
0.1%
Other values (191) 836
 
4.6%
(Missing) 11957
65.3%
ValueCountFrequency (%)
-1 12
 
0.1%
0 4620
25.2%
1 769
 
4.2%
2 3
 
< 0.1%
3 11
 
0.1%
4 8
 
< 0.1%
5 8
 
< 0.1%
6 13
 
0.1%
7 2
 
< 0.1%
8 8
 
< 0.1%
ValueCountFrequency (%)
993 3
< 0.1%
862 2
 
< 0.1%
825 3
< 0.1%
786 5
< 0.1%
758 2
 
< 0.1%
721 2
 
< 0.1%
614 3
< 0.1%
596 2
 
< 0.1%
554 3
< 0.1%
523 3
< 0.1%

r_offense_date
Date

MISSING 

Distinct1075
Distinct (%)12.8%
Missing9899
Missing (%)54.0%
Memory size143.2 KiB
Minimum2013-01-03 00:00:00
Maximum2016-12-03 00:00:00
2024-01-09T22:43:23.974844image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:24.116766image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

r_charge_desc
Text

MISSING 

Distinct341
Distinct (%)4.1%
Missing10039
Missing (%)54.8%
Memory size143.2 KiB
2024-01-09T22:43:24.322938image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length52
Median length38
Mean length25.374653
Min length6

Characters and Unicode

Total characters210026
Distinct characters72
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique56 ?
Unique (%)0.7%

Sample

1st rowFelony Battery (Dom Strang)
2nd rowDriving Under The Influence
3rd rowDriving Under The Influence
4th rowDriving Under The Influence
5th rowDriving Under The Influence
ValueCountFrequency (%)
theft 1227
 
4.2%
license 1051
 
3.6%
w/o 991
 
3.4%
of 828
 
2.8%
possess 821
 
2.8%
driving 759
 
2.6%
possession 731
 
2.5%
petit 701
 
2.4%
or 682
 
2.3%
violence 655
 
2.2%
Other values (583) 20943
71.3%
2024-01-09T22:43:24.664007image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 22059
 
10.5%
21127
 
10.1%
s 17802
 
8.5%
i 13506
 
6.4%
n 12990
 
6.2%
t 11406
 
5.4%
r 11348
 
5.4%
a 9331
 
4.4%
o 8861
 
4.2%
c 6149
 
2.9%
Other values (62) 75447
35.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 149238
71.1%
Uppercase Letter 30209
 
14.4%
Space Separator 21127
 
10.1%
Decimal Number 4226
 
2.0%
Other Punctuation 4153
 
2.0%
Currency Symbol 499
 
0.2%
Dash Punctuation 221
 
0.1%
Math Symbol 149
 
0.1%
Open Punctuation 102
 
< 0.1%
Close Punctuation 102
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 22059
14.8%
s 17802
11.9%
i 13506
9.0%
n 12990
8.7%
t 11406
 
7.6%
r 11348
 
7.6%
a 9331
 
6.3%
o 8861
 
5.9%
c 6149
 
4.1%
l 5063
 
3.4%
Other values (16) 30723
20.6%
Uppercase Letter
ValueCountFrequency (%)
O 3862
12.8%
P 3594
11.9%
D 2810
9.3%
L 2706
9.0%
T 2292
 
7.6%
C 2146
 
7.1%
S 2013
 
6.7%
V 1676
 
5.5%
W 1648
 
5.5%
G 1161
 
3.8%
Other values (14) 6301
20.9%
Decimal Number
ValueCountFrequency (%)
0 1794
42.5%
2 855
20.2%
1 713
 
16.9%
3 647
 
15.3%
6 63
 
1.5%
4 57
 
1.3%
5 52
 
1.2%
9 22
 
0.5%
8 21
 
0.5%
7 2
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
/ 4089
98.5%
, 56
 
1.3%
" 4
 
0.1%
. 4
 
0.1%
Math Symbol
ValueCountFrequency (%)
< 61
40.9%
> 58
38.9%
+ 30
20.1%
Space Separator
ValueCountFrequency (%)
21127
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 499
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 221
100.0%
Open Punctuation
ValueCountFrequency (%)
( 102
100.0%
Close Punctuation
ValueCountFrequency (%)
) 102
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 179447
85.4%
Common 30579
 
14.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 22059
 
12.3%
s 17802
 
9.9%
i 13506
 
7.5%
n 12990
 
7.2%
t 11406
 
6.4%
r 11348
 
6.3%
a 9331
 
5.2%
o 8861
 
4.9%
c 6149
 
3.4%
l 5063
 
2.8%
Other values (40) 60932
34.0%
Common
ValueCountFrequency (%)
21127
69.1%
/ 4089
 
13.4%
0 1794
 
5.9%
2 855
 
2.8%
1 713
 
2.3%
3 647
 
2.1%
$ 499
 
1.6%
- 221
 
0.7%
( 102
 
0.3%
) 102
 
0.3%
Other values (12) 430
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210026
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 22059
 
10.5%
21127
 
10.1%
s 17802
 
8.5%
i 13506
 
6.4%
n 12990
 
6.2%
t 11406
 
5.4%
r 11348
 
5.4%
a 9331
 
4.4%
o 8861
 
4.2%
c 6149
 
2.9%
Other values (62) 75447
35.9%

r_jail_in
Date

MISSING 

Distinct972
Distinct (%)15.3%
Missing11957
Missing (%)65.3%
Memory size143.2 KiB
Minimum2013-01-03 00:00:00
Maximum2016-12-02 00:00:00
2024-01-09T22:43:24.809226image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:24.951259image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

violent_recid
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing18316
Missing (%)100.0%
Memory size143.2 KiB

is_violent_recid
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size143.2 KiB
0
16977 
1
 
1339

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters18316
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 16977
92.7%
1 1339
 
7.3%

Length

2024-01-09T22:43:25.076874image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-09T22:43:25.173854image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 16977
92.7%
1 1339
 
7.3%

Most occurring characters

ValueCountFrequency (%)
0 16977
92.7%
1 1339
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 18316
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16977
92.7%
1 1339
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
Common 18316
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16977
92.7%
1 1339
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18316
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16977
92.7%
1 1339
 
7.3%

vr_charge_degree
Categorical

MISSING 

Distinct9
Distinct (%)0.7%
Missing16977
Missing (%)92.7%
Memory size143.2 KiB
(M1)
550 
(F3)
374 
(F2)
276 
(F1)
65 
(M2)
 
40
Other values (4)
 
34

Length

Max length5
Median length4
Mean length4.004481
Min length4

Characters and Unicode

Total characters5362
Distinct characters11
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row(F3)
2nd row(F2)
3rd row(F1)
4th row(F3)
5th row(F3)

Common Values

ValueCountFrequency (%)
(M1) 550
 
3.0%
(F3) 374
 
2.0%
(F2) 276
 
1.5%
(F1) 65
 
0.4%
(M2) 40
 
0.2%
(F7) 23
 
0.1%
(MO3) 6
 
< 0.1%
(F6) 4
 
< 0.1%
(F5) 1
 
< 0.1%
(Missing) 16977
92.7%

Length

2024-01-09T22:43:25.276467image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-09T22:43:25.383969image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
m1 550
41.1%
f3 374
27.9%
f2 276
20.6%
f1 65
 
4.9%
m2 40
 
3.0%
f7 23
 
1.7%
mo3 6
 
0.4%
f6 4
 
0.3%
f5 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
( 1339
25.0%
) 1339
25.0%
F 743
13.9%
1 615
11.5%
M 596
11.1%
3 380
 
7.1%
2 316
 
5.9%
7 23
 
0.4%
O 6
 
0.1%
6 4
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1345
25.1%
Open Punctuation 1339
25.0%
Close Punctuation 1339
25.0%
Decimal Number 1339
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 615
45.9%
3 380
28.4%
2 316
23.6%
7 23
 
1.7%
6 4
 
0.3%
5 1
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
F 743
55.2%
M 596
44.3%
O 6
 
0.4%
Open Punctuation
ValueCountFrequency (%)
( 1339
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1339
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4017
74.9%
Latin 1345
 
25.1%

Most frequent character per script

Common
ValueCountFrequency (%)
( 1339
33.3%
) 1339
33.3%
1 615
15.3%
3 380
 
9.5%
2 316
 
7.9%
7 23
 
0.6%
6 4
 
0.1%
5 1
 
< 0.1%
Latin
ValueCountFrequency (%)
F 743
55.2%
M 596
44.3%
O 6
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5362
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 1339
25.0%
) 1339
25.0%
F 743
13.9%
1 615
11.5%
M 596
11.1%
3 380
 
7.1%
2 316
 
5.9%
7 23
 
0.4%
O 6
 
0.1%
6 4
 
0.1%

vr_offense_date
Date

MISSING 

Distinct570
Distinct (%)42.6%
Missing16977
Missing (%)92.7%
Memory size143.2 KiB
Minimum2013-01-04 00:00:00
Maximum2016-11-01 00:00:00
2024-01-09T22:43:25.520256image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:25.658738image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

vr_charge_desc
Text

MISSING 

Distinct83
Distinct (%)6.2%
Missing16977
Missing (%)92.7%
Memory size143.2 KiB
2024-01-09T22:43:25.830160image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length37
Median length34
Mean length18.159074
Min length7

Characters and Unicode

Total characters24315
Distinct characters59
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)1.6%

Sample

1st rowFelony Battery (Dom Strang)
2nd rowAggrav Battery w/Deadly Weapon
3rd rowKidnapping (Facilitate Felony)
4th rowFelony Battery
5th rowFelony Battery
ValueCountFrequency (%)
battery 877
24.6%
aggravated 162
 
4.5%
assault 150
 
4.2%
weapon 137
 
3.8%
robbery 128
 
3.6%
123
 
3.4%
felony 114
 
3.2%
agg 98
 
2.7%
officer 82
 
2.3%
law 82
 
2.3%
Other values (139) 1617
45.3%
2024-01-09T22:43:26.152178image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 2534
 
10.4%
e 2303
 
9.5%
a 2261
 
9.3%
2242
 
9.2%
r 2005
 
8.2%
y 1244
 
5.1%
B 969
 
4.0%
g 961
 
4.0%
o 927
 
3.8%
n 913
 
3.8%
Other values (49) 7956
32.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 17774
73.1%
Uppercase Letter 3564
 
14.7%
Space Separator 2242
 
9.2%
Other Punctuation 489
 
2.0%
Close Punctuation 87
 
0.4%
Open Punctuation 87
 
0.4%
Decimal Number 55
 
0.2%
Dash Punctuation 9
 
< 0.1%
Math Symbol 8
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 2534
14.3%
e 2303
13.0%
a 2261
12.7%
r 2005
11.3%
y 1244
 
7.0%
g 961
 
5.4%
o 927
 
5.2%
n 913
 
5.1%
l 616
 
3.5%
d 572
 
3.2%
Other values (14) 3438
19.3%
Uppercase Letter
ValueCountFrequency (%)
B 969
27.2%
A 559
15.7%
W 317
 
8.9%
D 253
 
7.1%
F 236
 
6.6%
S 186
 
5.2%
E 133
 
3.7%
R 130
 
3.6%
P 129
 
3.6%
O 109
 
3.1%
Other values (12) 543
15.2%
Decimal Number
ValueCountFrequency (%)
1 14
25.5%
5 14
25.5%
6 13
23.6%
2 11
20.0%
7 2
 
3.6%
8 1
 
1.8%
Other Punctuation
ValueCountFrequency (%)
/ 486
99.4%
. 3
 
0.6%
Space Separator
ValueCountFrequency (%)
2242
100.0%
Close Punctuation
ValueCountFrequency (%)
) 87
100.0%
Open Punctuation
ValueCountFrequency (%)
( 87
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%
Math Symbol
ValueCountFrequency (%)
+ 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 21338
87.8%
Common 2977
 
12.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 2534
 
11.9%
e 2303
 
10.8%
a 2261
 
10.6%
r 2005
 
9.4%
y 1244
 
5.8%
B 969
 
4.5%
g 961
 
4.5%
o 927
 
4.3%
n 913
 
4.3%
l 616
 
2.9%
Other values (36) 6605
31.0%
Common
ValueCountFrequency (%)
2242
75.3%
/ 486
 
16.3%
) 87
 
2.9%
( 87
 
2.9%
1 14
 
0.5%
5 14
 
0.5%
6 13
 
0.4%
2 11
 
0.4%
- 9
 
0.3%
+ 8
 
0.3%
Other values (3) 6
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24315
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 2534
 
10.4%
e 2303
 
9.5%
a 2261
 
9.3%
2242
 
9.2%
r 2005
 
8.2%
y 1244
 
5.1%
B 969
 
4.0%
g 961
 
4.0%
o 927
 
3.8%
n 913
 
3.8%
Other values (49) 7956
32.7%

type_of_assessment
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size143.2 KiB
Risk of Recidivism
18316 

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters329688
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRisk of Recidivism
2nd rowRisk of Recidivism
3rd rowRisk of Recidivism
4th rowRisk of Recidivism
5th rowRisk of Recidivism

Common Values

ValueCountFrequency (%)
Risk of Recidivism 18316
100.0%

Length

2024-01-09T22:43:26.291515image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-09T22:43:26.385286image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
risk 18316
33.3%
of 18316
33.3%
recidivism 18316
33.3%

Most occurring characters

ValueCountFrequency (%)
i 73264
22.2%
R 36632
11.1%
s 36632
11.1%
36632
11.1%
k 18316
 
5.6%
o 18316
 
5.6%
f 18316
 
5.6%
e 18316
 
5.6%
c 18316
 
5.6%
d 18316
 
5.6%
Other values (2) 36632
11.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 256424
77.8%
Uppercase Letter 36632
 
11.1%
Space Separator 36632
 
11.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 73264
28.6%
s 36632
14.3%
k 18316
 
7.1%
o 18316
 
7.1%
f 18316
 
7.1%
e 18316
 
7.1%
c 18316
 
7.1%
d 18316
 
7.1%
v 18316
 
7.1%
m 18316
 
7.1%
Uppercase Letter
ValueCountFrequency (%)
R 36632
100.0%
Space Separator
ValueCountFrequency (%)
36632
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 293056
88.9%
Common 36632
 
11.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 73264
25.0%
R 36632
12.5%
s 36632
12.5%
k 18316
 
6.2%
o 18316
 
6.2%
f 18316
 
6.2%
e 18316
 
6.2%
c 18316
 
6.2%
d 18316
 
6.2%
v 18316
 
6.2%
Common
ValueCountFrequency (%)
36632
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 329688
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 73264
22.2%
R 36632
11.1%
s 36632
11.1%
36632
11.1%
k 18316
 
5.6%
o 18316
 
5.6%
f 18316
 
5.6%
e 18316
 
5.6%
c 18316
 
5.6%
d 18316
 
5.6%
Other values (2) 36632
11.1%

decile_score.1
Real number (ℝ)

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9970518
Minimum-1
Maximum10
Zeros0
Zeros (%)0.0%
Negative23
Negative (%)0.1%
Memory size143.2 KiB
2024-01-09T22:43:26.467681image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q12
median5
Q38
95-th percentile10
Maximum10
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation2.937569
Coefficient of variation (CV)0.58786043
Kurtosis-1.2452923
Mean4.9970518
Median Absolute Deviation (MAD)3
Skewness0.15158307
Sum91526
Variance8.6293115
MonotonicityNot monotonic
2024-01-09T22:43:26.567035image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 2923
16.0%
2 2031
11.1%
4 1830
10.0%
3 1813
9.9%
7 1720
9.4%
6 1713
9.4%
9 1670
9.1%
5 1649
9.0%
8 1614
8.8%
10 1330
7.3%
ValueCountFrequency (%)
-1 23
 
0.1%
1 2923
16.0%
2 2031
11.1%
3 1813
9.9%
4 1830
10.0%
5 1649
9.0%
6 1713
9.4%
7 1720
9.4%
8 1614
8.8%
9 1670
9.1%
ValueCountFrequency (%)
10 1330
7.3%
9 1670
9.1%
8 1614
8.8%
7 1720
9.4%
6 1713
9.4%
5 1649
9.0%
4 1830
10.0%
3 1813
9.9%
2 2031
11.1%
1 2923
16.0%

score_text
Categorical

Distinct3
Distinct (%)< 0.1%
Missing23
Missing (%)0.1%
Memory size143.2 KiB
Low
8597 
Medium
5082 
High
4614 

Length

Max length6
Median length4
Mean length4.0856612
Min length3

Characters and Unicode

Total characters74739
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLow
2nd rowLow
3rd rowMedium
4th rowLow
5th rowLow

Common Values

ValueCountFrequency (%)
Low 8597
46.9%
Medium 5082
27.7%
High 4614
25.2%
(Missing) 23
 
0.1%

Length

2024-01-09T22:43:26.679335image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-09T22:43:26.778725image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
low 8597
47.0%
medium 5082
27.8%
high 4614
25.2%

Most occurring characters

ValueCountFrequency (%)
i 9696
13.0%
L 8597
11.5%
o 8597
11.5%
w 8597
11.5%
M 5082
6.8%
e 5082
6.8%
d 5082
6.8%
u 5082
6.8%
m 5082
6.8%
H 4614
6.2%
Other values (2) 9228
12.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 56446
75.5%
Uppercase Letter 18293
 
24.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 9696
17.2%
o 8597
15.2%
w 8597
15.2%
e 5082
9.0%
d 5082
9.0%
u 5082
9.0%
m 5082
9.0%
g 4614
8.2%
h 4614
8.2%
Uppercase Letter
ValueCountFrequency (%)
L 8597
47.0%
M 5082
27.8%
H 4614
25.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 74739
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 9696
13.0%
L 8597
11.5%
o 8597
11.5%
w 8597
11.5%
M 5082
6.8%
e 5082
6.8%
d 5082
6.8%
u 5082
6.8%
m 5082
6.8%
H 4614
6.2%
Other values (2) 9228
12.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 74739
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 9696
13.0%
L 8597
11.5%
o 8597
11.5%
w 8597
11.5%
M 5082
6.8%
e 5082
6.8%
d 5082
6.8%
u 5082
6.8%
m 5082
6.8%
H 4614
6.2%
Other values (2) 9228
12.3%
Distinct703
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size143.2 KiB
Minimum2013-01-01 00:00:00
Maximum2014-12-31 00:00:00
2024-01-09T22:43:26.902323image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:27.042428image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

v_type_of_assessment
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size143.2 KiB
Risk of Violence
18316 

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters293056
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRisk of Violence
2nd rowRisk of Violence
3rd rowRisk of Violence
4th rowRisk of Violence
5th rowRisk of Violence

Common Values

ValueCountFrequency (%)
Risk of Violence 18316
100.0%

Length

2024-01-09T22:43:27.168498image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-09T22:43:27.261641image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
risk 18316
33.3%
of 18316
33.3%
violence 18316
33.3%

Most occurring characters

ValueCountFrequency (%)
i 36632
12.5%
36632
12.5%
o 36632
12.5%
e 36632
12.5%
R 18316
6.2%
s 18316
6.2%
k 18316
6.2%
f 18316
6.2%
V 18316
6.2%
l 18316
6.2%
Other values (2) 36632
12.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 219792
75.0%
Space Separator 36632
 
12.5%
Uppercase Letter 36632
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 36632
16.7%
o 36632
16.7%
e 36632
16.7%
s 18316
8.3%
k 18316
8.3%
f 18316
8.3%
l 18316
8.3%
n 18316
8.3%
c 18316
8.3%
Uppercase Letter
ValueCountFrequency (%)
R 18316
50.0%
V 18316
50.0%
Space Separator
ValueCountFrequency (%)
36632
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 256424
87.5%
Common 36632
 
12.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 36632
14.3%
o 36632
14.3%
e 36632
14.3%
R 18316
7.1%
s 18316
7.1%
k 18316
7.1%
f 18316
7.1%
V 18316
7.1%
l 18316
7.1%
n 18316
7.1%
Common
ValueCountFrequency (%)
36632
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 293056
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 36632
12.5%
36632
12.5%
o 36632
12.5%
e 36632
12.5%
R 18316
6.2%
s 18316
6.2%
k 18316
6.2%
f 18316
6.2%
V 18316
6.2%
l 18316
6.2%
Other values (2) 36632
12.5%

v_decile_score
Real number (ℝ)

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0228216
Minimum-1
Maximum10
Zeros0
Zeros (%)0.0%
Negative6
Negative (%)< 0.1%
Memory size143.2 KiB
2024-01-09T22:43:27.346351image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q12
median4
Q36
95-th percentile9
Maximum10
Range11
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.6141894
Coefficient of variation (CV)0.64983977
Kurtosis-0.7696677
Mean4.0228216
Median Absolute Deviation (MAD)2
Skewness0.55900485
Sum73682
Variance6.8339864
MonotonicityNot monotonic
2024-01-09T22:43:27.444993image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 4118
22.5%
2 2536
13.8%
3 2467
13.5%
4 2026
11.1%
5 1864
10.2%
6 1615
 
8.8%
7 1307
 
7.1%
8 994
 
5.4%
9 948
 
5.2%
10 435
 
2.4%
ValueCountFrequency (%)
-1 6
 
< 0.1%
1 4118
22.5%
2 2536
13.8%
3 2467
13.5%
4 2026
11.1%
5 1864
10.2%
6 1615
 
8.8%
7 1307
 
7.1%
8 994
 
5.4%
9 948
 
5.2%
ValueCountFrequency (%)
10 435
 
2.4%
9 948
 
5.2%
8 994
 
5.4%
7 1307
 
7.1%
6 1615
 
8.8%
5 1864
10.2%
4 2026
11.1%
3 2467
13.5%
2 2536
13.8%
1 4118
22.5%

v_score_text
Categorical

Distinct3
Distinct (%)< 0.1%
Missing6
Missing (%)< 0.1%
Memory size143.2 KiB
Low
11147 
Medium
4786 
High
2377 

Length

Max length6
Median length3
Mean length3.9139814
Min length3

Characters and Unicode

Total characters71665
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLow
2nd rowLow
3rd rowLow
4th rowLow
5th rowLow

Common Values

ValueCountFrequency (%)
Low 11147
60.9%
Medium 4786
26.1%
High 2377
 
13.0%
(Missing) 6
 
< 0.1%

Length

2024-01-09T22:43:27.554395image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-09T22:43:27.657070image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
low 11147
60.9%
medium 4786
26.1%
high 2377
 
13.0%

Most occurring characters

ValueCountFrequency (%)
L 11147
15.6%
o 11147
15.6%
w 11147
15.6%
i 7163
10.0%
M 4786
6.7%
e 4786
6.7%
d 4786
6.7%
u 4786
6.7%
m 4786
6.7%
H 2377
 
3.3%
Other values (2) 4754
6.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 53355
74.5%
Uppercase Letter 18310
 
25.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 11147
20.9%
w 11147
20.9%
i 7163
13.4%
e 4786
9.0%
d 4786
9.0%
u 4786
9.0%
m 4786
9.0%
g 2377
 
4.5%
h 2377
 
4.5%
Uppercase Letter
ValueCountFrequency (%)
L 11147
60.9%
M 4786
26.1%
H 2377
 
13.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 71665
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
L 11147
15.6%
o 11147
15.6%
w 11147
15.6%
i 7163
10.0%
M 4786
6.7%
e 4786
6.7%
d 4786
6.7%
u 4786
6.7%
m 4786
6.7%
H 2377
 
3.3%
Other values (2) 4754
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 71665
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
L 11147
15.6%
o 11147
15.6%
w 11147
15.6%
i 7163
10.0%
M 4786
6.7%
e 4786
6.7%
d 4786
6.7%
u 4786
6.7%
m 4786
6.7%
H 2377
 
3.3%
Other values (2) 4754
6.6%

priors_count.1
Real number (ℝ)

ZEROS 

Distinct39
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9131907
Minimum0
Maximum43
Zeros5175
Zeros (%)28.3%
Negative0
Negative (%)0.0%
Memory size143.2 KiB
2024-01-09T22:43:27.765242image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q35
95-th percentile15
Maximum43
Range43
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.2998641
Coefficient of variation (CV)1.3543588
Kurtosis5.9470361
Mean3.9131907
Median Absolute Deviation (MAD)2
Skewness2.2097383
Sum71674
Variance28.08856
MonotonicityNot monotonic
2024-01-09T22:43:27.891580image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0 5175
28.3%
1 3212
17.5%
2 2079
11.4%
3 1446
 
7.9%
4 1014
 
5.5%
5 897
 
4.9%
6 654
 
3.6%
7 607
 
3.3%
8 509
 
2.8%
9 456
 
2.5%
Other values (29) 2267
12.4%
ValueCountFrequency (%)
0 5175
28.3%
1 3212
17.5%
2 2079
11.4%
3 1446
 
7.9%
4 1014
 
5.5%
5 897
 
4.9%
6 654
 
3.6%
7 607
 
3.3%
8 509
 
2.8%
9 456
 
2.5%
ValueCountFrequency (%)
43 2
 
< 0.1%
39 1
 
< 0.1%
38 6
 
< 0.1%
37 1
 
< 0.1%
36 5
 
< 0.1%
35 3
 
< 0.1%
33 11
0.1%
31 14
0.1%
30 10
0.1%
29 20
0.1%

event
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size143.2 KiB
0
17497 
1
 
819

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters18316
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 17497
95.5%
1 819
 
4.5%

Length

2024-01-09T22:43:28.002467image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-09T22:43:28.098501image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 17497
95.5%
1 819
 
4.5%

Most occurring characters

ValueCountFrequency (%)
0 17497
95.5%
1 819
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 18316
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 17497
95.5%
1 819
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
Common 18316
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 17497
95.5%
1 819
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18316
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 17497
95.5%
1 819
 
4.5%

Interactions

2024-01-09T22:43:14.673141image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:42:59.760429image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:00.949276image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:02.257033image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:03.507875image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:04.787592image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:05.978908image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:07.203640image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:08.362803image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:09.732045image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:10.923305image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:12.112931image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:13.286844image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:14.760550image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:42:59.850556image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:01.042917image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:02.353346image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:03.591993image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:04.878145image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:06.072032image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:07.287431image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:08.457756image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:09.837225image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:11.010076image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:12.205792image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:13.377153image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:14.853825image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:42:59.946601image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:01.136795image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:02.453280image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:03.688046image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:04.974188image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:06.171247image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:07.382148image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:08.553458image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:09.921140image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:11.106202image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:12.298821image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:13.470849image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:14.952995image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:00.049157image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:01.238972image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:02.558462image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:03.784434image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:05.077038image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:06.273717image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:07.481482image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:08.655394image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:10.031188image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:11.205329image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:12.403723image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:13.572951image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:15.040381image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:00.137617image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:01.328463image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:02.651849image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:03.869183image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:05.170085image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:06.371856image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:07.565731image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:08.913968image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:10.120780image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:11.292709image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:12.490281image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:13.657715image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:15.130557image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:00.227738image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:01.526569image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:02.748023image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:03.960187image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:05.263088image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:06.468755image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:07.653414image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:09.007128image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:10.210775image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:11.384781image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:12.584371image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:13.748334image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:15.229738image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:00.325028image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:01.625815image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:02.847160image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:04.053339image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:05.362074image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:06.564873image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:07.752449image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:09.100328image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:10.309827image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:11.471951image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:12.678033image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:13.844859image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:15.310968image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:00.410892image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:01.712388image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:02.941411image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:04.138387image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:05.446847image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:06.653061image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:07.833986image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:09.191144image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:10.395734image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:11.565733image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:12.762208image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:13.929459image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:15.406691image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:00.510011image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:01.808530image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:03.041126image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:04.233804image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:05.540475image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:06.749208image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:07.924130image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:09.284440image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:10.486042image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:11.659344image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:12.858267image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:14.235740image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:15.488911image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:00.606147image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:01.896057image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:03.131501image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:04.315312image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:05.627260image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:06.838906image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:08.008679image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:09.376630image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:10.571145image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:11.755789image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:12.940627image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:14.320392image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:15.585051image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:00.693312image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:01.989374image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:03.225314image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:04.402638image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:05.717399image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:06.926653image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:08.104869image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:09.470899image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:10.664314image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:11.845955image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:13.030777image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:14.411621image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:15.672303image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:00.780326image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:02.078968image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:03.321689image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:04.489860image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:05.807535image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:07.016778image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:08.191928image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:09.561131image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:10.751492image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:11.931850image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:13.114911image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:14.501809image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:15.760221image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:00.865129image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:02.171809image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:03.414746image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:04.576412image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:05.895335image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:07.112838image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:08.276625image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:09.654258image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:10.839401image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:12.019444image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:13.204919image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T22:43:14.588955image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Missing values

2024-01-09T22:43:15.944475image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-09T22:43:16.462377image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idnamefirstlastsexdobageage_catracejuv_fel_countdecile_scorejuv_misd_countjuv_other_countpriors_countdays_b_screening_arrestc_jail_inc_jail_outc_days_from_compasc_charge_degreec_charge_descis_recidr_charge_degreer_days_from_arrestr_offense_dater_charge_descr_jail_inviolent_recidis_violent_recidvr_charge_degreevr_offense_datevr_charge_desctype_of_assessmentdecile_score.1score_textscreening_datev_type_of_assessmentv_decile_scorev_score_textpriors_count.1event
01.0miguel hernandezmiguelhernandezMale18/04/194769Greater than 45Other01000-1.013/08/2013 6:0314/08/2013 5:411.0(F3)Aggravated Assault w/Firearm0NaNNaNNaNNaNNaNNaN0NaNNaNNaNRisk of Recidivism1Low14/08/2013Risk of Violence1Low00
12.0miguel hernandezmiguelhernandezMale18/04/194769Greater than 45Other01000-1.013/08/2013 6:0314/08/2013 5:411.0(F3)Aggravated Assault w/Firearm0NaNNaNNaNNaNNaNNaN0NaNNaNNaNRisk of Recidivism1Low14/08/2013Risk of Violence1Low00
23.0michael ryanmichaelryanMale06/02/19853125 - 45Caucasian05000NaNNaNNaNNaNNaNNaN-1NaNNaNNaNNaNNaNNaN0NaNNaNNaNRisk of Recidivism5Medium31/12/2014Risk of Violence2Low00
34.0kevon dixonkevondixonMale22/01/19823425 - 45African-American03000-1.026/01/2013 3:4505/02/2013 5:361.0(F3)Felony Battery w/Prior Convict1(F3)NaN05/07/2013Felony Battery (Dom Strang)NaNNaN1(F3)05/07/2013Felony Battery (Dom Strang)Risk of Recidivism3Low27/01/2013Risk of Violence1Low01
45.0ed philoedphiloMale14/05/199124Less than 25African-American04014-1.013/04/2013 4:5814/04/2013 7:021.0(F3)Possession of Cocaine1(M1)0.016/06/2013Driving Under The Influence16/06/2013NaN0NaNNaNNaNRisk of Recidivism4Low14/04/2013Risk of Violence3Low40
56.0ed philoedphiloMale14/05/199124Less than 25African-American04014-1.013/04/2013 4:5814/04/2013 7:021.0(F3)Possession of Cocaine1(M1)0.016/06/2013Driving Under The Influence16/06/2013NaN0NaNNaNNaNRisk of Recidivism4Low14/04/2013Risk of Violence3Low40
67.0ed philoedphiloMale14/05/199124Less than 25African-American04014-1.013/04/2013 4:5814/04/2013 7:021.0(F3)Possession of Cocaine1(M1)0.016/06/2013Driving Under The Influence16/06/2013NaN0NaNNaNNaNRisk of Recidivism4Low14/04/2013Risk of Violence3Low40
78.0ed philoedphiloMale14/05/199124Less than 25African-American04014-1.013/04/2013 4:5814/04/2013 7:021.0(F3)Possession of Cocaine1(M1)0.016/06/2013Driving Under The Influence16/06/2013NaN0NaNNaNNaNRisk of Recidivism4Low14/04/2013Risk of Violence3Low40
89.0ed philoedphiloMale14/05/199124Less than 25African-American04014-1.013/04/2013 4:5814/04/2013 7:021.0(F3)Possession of Cocaine1(M1)0.016/06/2013Driving Under The Influence16/06/2013NaN0NaNNaNNaNRisk of Recidivism4Low14/04/2013Risk of Violence3Low40
910.0marcu brownmarcubrownMale21/01/199323Less than 25African-American08101NaNNaNNaN1.0(F3)Possession of Cannabis0NaNNaNNaNNaNNaNNaN0NaNNaNNaNRisk of Recidivism8High13/01/2013Risk of Violence6Medium10
idnamefirstlastsexdobageage_catracejuv_fel_countdecile_scorejuv_misd_countjuv_other_countpriors_countdays_b_screening_arrestc_jail_inc_jail_outc_days_from_compasc_charge_degreec_charge_descis_recidr_charge_degreer_days_from_arrestr_offense_dater_charge_descr_jail_inviolent_recidis_violent_recidvr_charge_degreevr_offense_datevr_charge_desctype_of_assessmentdecile_score.1score_textscreening_datev_type_of_assessmentv_decile_scorev_score_textpriors_count.1event
18306NaNraheem smithraheemsmithMale28/06/199520Less than 25African-American09000-1.019/10/2013 11:1720/10/2013 8:131.0(F3)Possession of Cocaine0NaNNaNNaNNaNNaNNaN0NaNNaNNaNRisk of Recidivism9High20/10/2013Risk of Violence9High00
18307NaNraheem smithraheemsmithMale28/06/199520Less than 25African-American09000-1.019/10/2013 11:1720/10/2013 8:131.0(F3)Possession of Cocaine0NaNNaNNaNNaNNaNNaN0NaNNaNNaNRisk of Recidivism9High20/10/2013Risk of Violence9High00
18308NaNraheem smithraheemsmithMale28/06/199520Less than 25African-American09000-1.019/10/2013 11:1720/10/2013 8:131.0(F3)Possession of Cocaine0NaNNaNNaNNaNNaNNaN0NaNNaNNaNRisk of Recidivism9High20/10/2013Risk of Violence9High00
18309NaNsteven butlerstevenbutlerMale17/07/199223Less than 25African-American07000-1.022/11/2013 5:1824/11/2013 2:591.0(F3)Deliver Cannabis0NaNNaNNaNNaNNaNNaN0NaNNaNNaNRisk of Recidivism7Medium23/11/2013Risk of Violence5Medium00
18310NaNmalcolm simmonsmalcolmsimmonsMale25/03/199323Less than 25African-American03000-1.031/01/2014 7:1302/02/2014 4:031.0(F3)Leaving the Scene of Accident0NaNNaNNaNNaNNaNNaN0NaNNaNNaNRisk of Recidivism3Low01/02/2014Risk of Violence5Medium00
18311NaNalexsandra beauchampsalexsandrabeauchampsFemale21/12/19843125 - 45African-American06005-1.028/12/2014 10:1407/01/2015 11:421.0(M1)Battery0NaNNaNNaNNaNNaNNaN0NaNNaNNaNRisk of Recidivism6Medium29/12/2014Risk of Violence4Low50
18312NaNwinston gregorywinstongregoryMale01/10/195857Greater than 45Other01000-1.013/01/2014 5:4814/01/2014 7:491.0(F2)Aggravated Battery / Pregnant0NaNNaNNaNNaNNaNNaN0NaNNaNNaNRisk of Recidivism1Low14/01/2014Risk of Violence1Low00
18313NaNfarrah jeanfarrahjeanFemale17/11/19823325 - 45African-American02003-1.008/03/2014 8:0609/03/2014 12:181.0(M1)Battery on Law Enforc Officer0NaNNaNNaNNaNNaNNaN0NaNNaNNaNRisk of Recidivism2Low09/03/2014Risk of Violence2Low30
18314NaNflorencia sanmartinflorenciasanmartinFemale18/12/199223Less than 25Hispanic04002-2.028/06/2014 12:1630/06/2014 11:192.0(F3)Possession of Ethylone1(M2)0.015/03/2015Operating W/O Valid License15/03/2015NaN0NaNNaNNaNRisk of Recidivism4Low30/06/2014Risk of Violence4Low20
18315NaNflorencia sanmartinflorenciasanmartinFemale18/12/199223Less than 25Hispanic04002-2.028/06/2014 12:1630/06/2014 11:192.0(F3)Possession of Ethylone1(M2)0.015/03/2015Operating W/O Valid License15/03/2015NaN0NaNNaNNaNRisk of Recidivism4Low30/06/2014Risk of Violence4Low20

Duplicate rows

Most frequently occurring

idnamefirstlastsexdobageage_catracejuv_fel_countdecile_scorejuv_misd_countjuv_other_countpriors_countdays_b_screening_arrestc_jail_inc_jail_outc_days_from_compasc_charge_degreec_charge_descis_recidr_charge_degreer_days_from_arrestr_offense_dater_charge_descr_jail_inis_violent_recidvr_charge_degreevr_offense_datevr_charge_desctype_of_assessmentdecile_score.1score_textscreening_datev_type_of_assessmentv_decile_scorev_score_textpriors_count.1event# duplicates
161NaNbrandon rossbrandonrossMale17/02/19853125 - 45Caucasian03001-1.004/02/2013 7:2415/03/2013 8:191.0(F3)Possession of Cocaine1(M2)0.014/04/2013Obstuct By Solicitation14/04/20130NaNNaNNaNRisk of Recidivism3Low05/02/2013Risk of Violence2Low1013
1481NaNvalerie agostinovalerieagostinoFemale21/11/199124Less than 25Caucasian010009-1.009/04/2013 9:1418/04/2013 6:221.0(M1)Possess Drug Paraphernalia1(M1)0.021/04/2013Possess Drug Paraphernalia21/04/20130NaNNaNNaNRisk of Recidivism10High10/04/2013Risk of Violence8High9013
852NaNkimani ogarrokimaniogarroMale08/05/199322Less than 25African-American03000-1.011/02/2013 6:1712/02/2013 8:091.0(F3)Grand Theft in the 3rd Degree1(M1)NaN17/06/2013Trespass Other Struct/ConveyNaN0NaNNaNNaNRisk of Recidivism3Low12/02/2013Risk of Violence5Medium0011
112NaNashley riveraashleyriveraFemale31/08/199124Less than 25Caucasian08000NaNNaNNaNNaNNaNNaN-1NaNNaNNaNNaNNaN0NaNNaNNaNRisk of Recidivism8High01/05/2013Risk of Violence5Medium009
292NaNdaniel diazdanieldiazMale05/04/196650Greater than 45Hispanic09003-1.019/01/2013 7:3522/02/2013 6:461.0(F3)Possession of Cocaine1(F3)0.003/05/2013Tampering With Physical Evidence03/05/20130NaNNaNNaNRisk of Recidivism9High20/01/2013Risk of Violence4Low309
1080NaNneville henrynevillehenryMale02/11/199223Less than 25African-American09015-1.006/03/2013 5:5711/03/2013 5:351.0(F3)Grand Theft in the 3rd Degree1(MO3)0.011/04/2013Retail Theft11/04/20131(M1)06/01/2015BatteryRisk of Recidivism9High07/03/2013Risk of Violence9High509
89NaNanton bryantantonbryantMale10/06/19833225 - 45African-American090013-1.024/02/2013 8:4404/04/2013 4:041.0(M1)Possess Cannabis/20 Grams Or Less1(M2)NaN22/04/2013Susp Drivers Lic 1st OffenseNaN0NaNNaNNaNRisk of Recidivism9High25/02/2013Risk of Violence8High1308
199NaNcarl toussaintcarltoussaintMale07/09/19833225 - 45African-American04005-1.017/01/2013 9:4218/01/2013 10:001.0(M1)arrest case no charge0NaNNaNNaNNaNNaN0NaNNaNNaNRisk of Recidivism4Low18/01/2013Risk of Violence4Low508
241NaNchristian stubbschristianstubbsMale15/01/19902625 - 45African-American08002-1.025/04/2013 11:4926/04/2013 7:291.0(F3)Possession of Cocaine1(M2)36.001/06/2013Driving License Suspended07/07/20130NaNNaNNaNRisk of Recidivism8High26/04/2013Risk of Violence8High208
376NaNdeshawn isaacdeshawnisaacMale03/04/199620Less than 25African-American06000-1.009/09/2014 5:5111/09/2014 3:351.0(F3)Pos Cannabis W/Intent Sel/Del1(M1)0.030/10/2014Trespass After Warning30/10/20140NaNNaNNaNRisk of Recidivism6Medium10/09/2014Risk of Violence7Medium008